Open Source Linux Algorithms - Page 27

A genetic algorithm framework to allow the evolution of synapse weights and topologies of artificial neural networks.

GAKNN is a data mining software for gene annotation data. GAKNN is built with k- Nearest Neighbour algorithm optimized by the genetic algorithm. Gene annotation datasets saved under .csv or .arff formats with Gene Ontology or FunCat categorization can use GAKNN to predict gene functions.

A class/library which uses the graphic card for linear algebra. Warning : you will need a GeForce 8 serie to take profit of this class.

General Toolkit Library is a general purpose toolkit written in C++ designed to make it easier for developers to produce applications. It provides classes and templates that implement several data structures like Arrays, Lists, Heaps, Sets, Files, etc.

The following code is a parallel kNN implementation that uses GPUs for the high dimensional data in text classification. You can use it to classify documents using kNN or to generate meta-features based on the distances between a query document and its k nearest neigbors

Galapagos is a Genetic Algorithm framework written in Java 5 with the intended audience of undergraduates in an Artificial Intelligence class. The goal of Galapagos is usability: a competent student should be able to learn this library in an afternoon.

(REW) French Project (Research Encadred Work)... Chinese Games (Halma) created by students in License... Interesting by modifiable dynamicly AI and concept. Report, PPT, API and Install included...

The C++ library Geneva allows to run large scale parametric optimization problems. It can run in serial or multi-threaded mode or in a networked environment. The library currently covers Evolutionary Strategies, Genetic Algorithms and mixed scenarios.

A generic implementation of STL containers and some STL algorithms. The main intent is no make this STL implementation to work with any kind of pointers defined by allocator classes, e.g. memory_mgr::offset_ptr.

Implementation of paralell partitioning algorithms achieving an optimal number of comparisons. See L. Frias and J. Petit. Parallel partition revisited. http://dx.doi.org/10.1007/978-3-540-68552-4_11 for further reference.

NOTE: Project has been delayed due to other tasks. Genetic Algorithms General Solver (GAGENES) is a C++ implementation of the genetic algorithm concept.

This is a cross-platform framework for using Genetic Algorithms for solutions. Written in Java and uses convinient plug-in features for every phase in the genetic development, while maintaining an easy-to-use API for easy integration into applications.

This project aims to create an open source genetic algorithms (GA) library for the IBM Cell BE processor. A well-documented implementation of GAs is provided, along with a number of sample fitness functions illustrating how to use the GA library.

This is an attempt to use Genetic Programming for writing a go player in a pseudo-ASM. It is entirely written in Common Lisp.

Genetic Programming Classifier is a distributed evolutionary data classification program. It uses the ensemble method implemented under a parallel co-evolutionary Genetic Programming technique.

Genetic Programming in OpenCL is a parallel implementation of genetic programming targeted at heterogeneous devices, such as CPU and GPU. It is written in OpenCL, an open standard for portable parallel programming across many computing platforms.

This project aims at providing a set of tools for solving the class of monodimensional packing problems (such as cutting stock, bin packing and knapsack problem) mainly using genetic algoritms.

Geneur is an Open Source scheduler for GRID. It is based on variation of genetic algorithms. Geneur uses backfill scheduling algorithm to create first genetic population.

A C++ RAII ( Resource Allocation Is Initialization ) implementation of an automatic pointer/reference. Pointable/Referenced objects are handled in a manner similar to Python. Strives to have the simplest syntax possible.

in-place sorting algorithm with O(n*log(k)) time komplexity. The data have to be the structure: struct { unsigned (char/short/int/long); [...] }

The game of Cows-n-Bulls is back ! Gnu-Moo aims to provide a command line as well as GTK+ front-end for the user to play the cows-n-bulls game. Multi-game sessions and relevant session stats are provided, which can be useful to test heuristic algorithms.

GoDS, which means "Go Data Structures", is an implementation of various data structures and algorithms in Go. It provides a wide variety of containers (Sets, Lists, Stacks, Maps, Trees), sets (HashSet, TreeSet, LinkedHashSet), lists (ArrayList, SinglyLinkedList, DoublyLinkedList), stacks (LinkedListStack, ArrayStack), maps (HashMap, TreeMap, HashBidiMap, TreeBidiMap, LinkedHashMap), trees (RedBlackTree, AVLTree, BTree, BinaryHeap), comparators, and iterators. Containers are either ordered or unordered. All ordered containers provide stateful iterators and some of them allow enumerable functions. A list is a data structure that stores values and may have repeated values. A list backed by a dynamic array that grows and shrinks implicitly. A list where each element points to the next element in the list. A set is a data structure that can store elements and has no repeated values. A Map is a data structure that maps keys to values.

This project aims to provide developers a convenient library about graph theory algorithm and some basic data structures. It will be developed in Java via Eclipse IDE, supporting both win32 and linux systems, and published as jar files.

GraphEmbedding is an open-source Python project for implementing and experimenting with graph embedding algorithms. It follows a simple “graph in, embedding out” design. The library uses NetworkX graphs as input and produces vector representations for graph nodes. It includes implementations of DeepWalk, LINE, Node2Vec, SDNE, and Struc2Vec. Users can configure walks, embedding dimensions, training windows, epochs, and other model-specific parameters. Example scripts demonstrate how to train models and retrieve embeddings for downstream graph analysis or machine learning tasks.